Searching through text for trends or identifying issues is extremely important; thus, labelling the text is a necessary but arduous task. It's made only harder when needing to relate differing responses across subjects.
This project aims to alleviate that difficulty through the use of BERT, a sentence embedding ML model.
- Get analysis on standups
- Plot the standups to be easily grouped
- Hover over clusters to read their messages
This project is built with standupman, BentoML and streamlit
You can choose to run your own instance of standupman or use another to get data for anaylsis.
- Install MongoDB
- Establish Database Connection
- mongosh
- show dbs
- use scrum_app
- python3 client/data.py
- cd client
- python3 streamlit main.py
cd client
pip install -r requirements.txtcd client
streamlit main.pycd standupman
npm installcd standupman
npm run dev- Streamlit
- Standupman API
- Linode
- GitHub Actions
 Alesana Lealofi Eteuati Jr |
 Spencer Churchill |
 Huilun Ang |